Efficient One-Step Orthogonal Consensus Framework for Multiview Remote Sensing Clustering

Jointly clustering multi-source remote sensing (RS) data remains challenging despite the promising progress of multi-view clustering (MVC). Many existing methods have several limitations: 1) two-stage pipelines that separate feature learning from clustering often incur error accumulation and lead to blurred land-cover boundaries; 2) fixed or equal-view fusion cannot adapt to heterogeneous view quality, allowing noisy or redundant views to dominate the consensus representation; and 3) the construction of full affinity matrices results in high computational cost, restricting scalability for large RS data. To address these issues, we propose an Efficient One-step Orthogonal Consensus (EO2C) framework for multi-view RS clustering. EO2C first performs superpixel-guided spatial denoising to obtain reliable and spatially coherent features. It then learns structure-consistent embeddings by jointly optimizing the embedding, mapping matrix, and cluster indicator matrix, thereby preserving view-specific local structures. More importantly, an adaptive consensus fusion strategy based on a collective orthogonality constraint projects reconstructed view-specific representations into a shared consensus space while automatically regulating view contributions to suppress unreliable views. These components collectively enable EO2C to achieve robust, discriminative, and one-step clustering. An efficient alternating optimization algorithm further attains near-linear computational complexity via closed-form or orthogonal updates. Extensive experiments on four real-world multi-source RS datasets demonstrate that EO2C outperforms state-of-the-art methods in clustering performance. The code is available at https://github.com/ytccyw/EO2C.